Prospective abuse and intimate partner violence surgical evaluation (PRAISE-2 pilot): Statistical analysis plan for a pilot prospective cohort study
Bibliographic record
Abstract
Background. Intimate partner violence (IPV) is a prevalent social issue that affects the health and well-being of women globally. In orthopaedics, the prevalence of women who have experienced abuse in the past year is as high as 1 in 6. PRAISE-2 is a multi-centre pilot prospective cohort study of 250 women with musculoskeletal injuries to determine how IPV experiences affect injury-related outcomes, and how patterns of IPV change over a 12 month period of time following a musculoskeletal injury. The current report is a description of the statistical analysis plan for the PRAISE-2 pilot study. Methods. This study is a pilot multicentre prospective cohort study to primarily assess feasibility of our recruitment, retention and data collection strategies, and to collect preliminary data on orthopaedic outcomes after experiencing IPV, as well as changes in IPV patterns following an injury. Included participants will be adult females presenting to participating fracture clinics for a fracture and/or dislocation requiring orthopaedic care. Participants will be followed for one year. The primary analysis will be descriptive. We will report recruitment, missed visits, out of window visits, participant completion data, and completed form data as counts and percentages with 95% confidence intervals. Based on the primary analyses, we will report whether the feasibility criteria have been met, and recommend modifications to the protocol for any planned definitive studies, if needed. All secondary (clinical) analyses are exploratory. Discussion. In order for surgeons to be as effective as possible in assisting and advocating for women who have experienced abuse, we need more information on how IPV experiences are associated with musculoskeletal outcomes. Both the feasibility and clinical information gained from this pilot study will be instrumental in informing future observational and interventional IPV studies. By reporting our statistical analysis plan before the study ends, we hope to improve the transparency, integrity, and reproducibility of our study findings. Trial registration. This study is registered on clinicaltrials.gov NCT02529267 on 20 August 2015, before the first participant was enrolled
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.132 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".